Transformer area reactive power optimization method and related device

Abstract

The invention discloses a transformer area reactive power optimization method and a related device, and relates to the field of power systems. The method comprises the following steps: acquiring first data of a photovoltaic inverter, second data of a user ammeter and third data of a transformer area, determining a photovoltaic fluctuation characteristic from the first data, determining a load characteristic from the second data, and determining a voltage out-of-limit characteristic from the third data; calculating the reactive compensation demand of each node according to the photovoltaic fluctuation characteristics, the load characteristics, the voltage out-of-limit characteristics and the transformer area topological structure; constructing a multi-objective optimization function according to the voltage deviation, the light abandoning rate and the line loss, and generating a global optimal reactive compensation strategy according to the multi-objective optimization function and the reactive compensation demand of each node; and decomposing the global optimal reactive power compensation strategy into switching instructions of the reactive power compensation devices for execution. According to the method, reactive compensation requirements are accurately calculated, a global optimal strategy is generated, reactive power of the transformer area is effectively optimized, and voltage deviation, light abandoning rate and line loss are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a transformer area reactive power optimization method and related device. BACKGROUND

[0002] With the rapid development of social economy, the stability and quality of power supply are increasingly concerned. Especially in the transformer area part of the distribution network, its operating condition directly affects the user's power experience and the overall efficiency of the power grid. In recent years, a large number of distributed power sources are connected, especially the widespread application of photovoltaic power generation, which makes the power quality of the transformer area face new challenges. At the same time, the user's power consumption behavior is increasingly complex, and the load characteristics are variable, which further increases the difficulty of transformer area reactive power management.

[0003] In the prior art, for the transformer area reactive power optimization problem, the conventional solutions mainly include the following. One is to use a fixed capacity reactive power compensation device, such as a capacitor bank. This method uses a pre-set compensation capacity to switch at a specific time to achieve a certain degree of reactive power compensation. Another common method is to use a traditional reactive power control strategy based on single-point measurement, which controls the action of the reactive power compensation device according to the change of a single parameter such as voltage or power factor at a certain monitoring point. In addition, some areas use manual regular inspection and adjustment of the reactive power compensation device to maintain the reactive power balance of the transformer area.

[0004] However, these existing conventional methods have obvious defects. The fixed capacity reactive power compensation device cannot adapt to the photovoltaic output fluctuation, load change, voltage out-of-limit and other complex situations, and cannot accurately meet the actual reactive power compensation demand of each node. The traditional reactive power control strategy based on single-point measurement ignores the topology structure of the entire transformer area and the mutual influence between nodes, which easily leads to the problem of excessive or insufficient local compensation. The manual inspection and adjustment method is not only inefficient, but also difficult to achieve real-time response and fine management. SUMMARY

[0005] The present application provides a transformer area reactive power optimization method and related device, which accurately calculates the reactive power compensation demand, generates a globally optimal strategy, and realizes effective optimization of the transformer area reactive power, thereby reducing voltage deviation, light rejection rate and line loss.

[0006] In a first aspect of the present application, a transformer area reactive power optimization method is provided, applied to a transformer area reactive power optimization platform, and the method comprises:

[0007] acquire first data of a photovoltaic inverter, second data of a user electricity meter and third data of a transformer area, determine photovoltaic fluctuation characteristics from the first data, determine load characteristics from the second data, and determine voltage out-of-limit characteristics from the third data, the photovoltaic fluctuation characteristics including photovoltaic output fluctuation rate, change slope, and intra-day fluctuation amplitude, the load characteristics including three-phase unbalance degree and peak-valley difference, and the voltage out-of-limit characteristics including duration, amplitude and frequency of voltage out-of-limit;

[0008] calculate reactive power compensation demand of each node according to the photovoltaic fluctuation characteristics, the load characteristics, the voltage out-of-limit characteristics and transformer area topology;

[0009] construct a multi-objective optimization function according to voltage deviation, light rejection rate and line loss, and generate a globally optimal reactive power compensation strategy according to the multi-objective optimization function and the reactive power compensation demand of each node;

[0010] decompose the globally optimal reactive power compensation strategy into switching instructions of each reactive power compensation device for execution.

[0011] By using the above technical solutions, through data-driven feature extraction, multi-factor collaborative modeling, multi-objective optimization and device-level control, the voltage quality, new energy utilization rate and power grid efficiency are significantly improved, and the operation and maintenance cost is reduced, thereby providing an innovative solution for transformer area reactive power optimization under high penetration of new energy.

[0012] Optionally, the calculation of the reactive power compensation demand of each node according to the photovoltaic fluctuation characteristics, the load characteristics, the voltage out-of-limit characteristics and the transformer area topology comprises:

[0013] forming a state description of each node according to the photovoltaic fluctuation characteristics, the load characteristics, the voltage out-of-limit characteristics and the transformer area topology;

[0014] calculating a preliminary reactive power compensation demand of each node according to the state description, and adjusting the preliminary reactive power compensation demand of each node according to the transformer area topology;

[0015] weighting and summing the adjusted photovoltaic fluctuation compensation demand, three-phase unbalance compensation demand and voltage out-of-limit compensation demand to obtain the reactive power compensation demand of each node.

[0016] By adopting the technical scheme, the photovoltaic fluctuation characteristics, the load characteristics, the voltage out-of-limit characteristics and the transformer area topology structure are used to form a node-level state description, and the operation characteristics of each node are quantitatively reflected. The state description is updated in real time, and is adapted to the rapid changes of photovoltaic output and load demand, thereby providing an accurate basis for subsequent calculation. Based on the node state description, the initial reactive power compensation demand is calculated, and a differentiated compensation scheme is provided for the photovoltaic fluctuation, the load characteristics and the voltage out-of-limit risk of different nodes. The initial demand is adjusted through the transformer area topology structure, the transmission path of the reactive power in the power grid is optimized, the transmission loss is reduced, and the compensation efficiency is improved. The photovoltaic fluctuation compensation demand, the three-phase imbalance compensation demand and the voltage out-of-limit compensation demand are weighted and summed, and the new energy consumption, the power grid stability and the voltage quality are comprehensively considered, thereby avoiding secondary problems caused by single demand domination. The demand priority is dynamically adjusted through the weighting coefficient, and the compensation target in different scenarios is adapted.

[0017] Optionally, the forming of the state description of each node according to the photovoltaic fluctuation characteristics, the load characteristics, the voltage out-of-limit characteristics and the transformer area topology structure comprises:

[0018] The photovoltaic fluctuation characteristics are mapped to photovoltaic grid-connected nodes, the load characteristics are mapped to user load nodes, and the voltage out-of-limit characteristics are mapped to voltage out-of-limit nodes, so as to determine the characteristic description of each node.

[0019] According to the transformer area topology structure, the electrical connection relationship between each node and other nodes is determined, and the reactive power transmission loss between nodes is calculated.

[0020] The state description of each node is determined according to the characteristic description, the electrical connection relationship and the reactive power transmission loss.

[0021] By adopting the technical scheme, the photovoltaic fluctuation characteristics, the load characteristics, the voltage out-of-limit characteristics are respectively mapped to photovoltaic grid-connected nodes, user load nodes and voltage out-of-limit nodes, the accurate correspondence between the characteristics and the nodes is realized, and the calculation error caused by the characteristic confusion is avoided. Through the characteristic mapping, the photovoltaic fluctuation, the load characteristics and the voltage out-of-limit risk of each node are quantified, and a data basis is provided for subsequent state analysis. Based on the transformer area topology structure, the electrical connection relationship (such as the path and the impedance) between the nodes is determined, and the transmission path of the reactive power in the power grid is determined. The reactive power transmission loss between the nodes is calculated, the influence of the power grid structure on the reactive compensation efficiency is reflected, and a basis for optimizing the transmission path is provided. The node-level state description is formed by combining the characteristic description (the photovoltaic fluctuation, the load characteristics and the voltage out-of-limit), the electrical connection relationship and the reactive power transmission loss, and the operation state and the power grid constraint of the node are comprehensively reflected. The state description is adjusted in real time with the characteristic update and the topology change, and is adapted to the dynamic scenarios such as the photovoltaic output fluctuation and the load demand change.

[0022] Optionally, the calculating the preliminary reactive power compensation demand of each node according to the state description comprises:

[0023] According to the mapping relationship between the characteristics and the reactive power compensation demand, the basic compensation demand of each characteristic in each node is calculated, and a plurality of the basic compensation demands are weighted and summed to obtain the preliminary reactive power compensation demand of each node;

[0024] The power flow calculation method is used to simulate the state of the power grid after reactive power compensation, and the reactive power distribution result of each node and the voltage out-of-limit information of adjacent nodes are calculated;

[0025] The preliminary reactive power compensation demand of each node is adjusted according to the voltage out-of-limit information of adjacent nodes.

[0026] By adopting the above technical solution, based on the mapping relationship between the characteristics and the reactive power compensation demand, the basic compensation demand of characteristics such as photovoltaic fluctuation, load characteristics, and voltage out-of-limit in each node is quantified, and the demand calculation is highly matched with the actual characteristics. By weighted summing the multiple characteristic demands, the preliminary reactive power compensation demand is generated, avoiding insufficient or excessive compensation caused by single characteristic dominance. The power flow calculation method is used to simulate the state of the power grid after reactive power compensation, and the reactive power distribution of each node is accurately calculated to verify the influence of the compensation strategy on the voltage and current of the power grid. Through the power flow calculation result, the voltage out-of-limit information (such as out-of-limit amplitude and duration) of adjacent nodes is detected, and the secondary problems that may be caused by the compensation strategy are clarified to provide a basis for subsequent adjustment. According to the voltage out-of-limit information of adjacent nodes, the preliminary reactive power compensation demand of each node is dynamically adjusted to reduce the influence of the compensation strategy on other parts of the power grid, and the global voltage quality is ensured. Through the analysis of the distribution network topology structure, the transmission path of the reactive power in the power grid is optimized, the transmission loss is reduced, and the compensation efficiency is improved.

[0027] Optionally, the calculating the reactive power distribution result of each node and the voltage out-of-limit information of adjacent nodes comprises:

[0028] Taking the distribution network topology structure and the initial reactive power compensation demand of each node as input, the voltage amplitude of each node is iteratively solved by Newton-Raphson method;

[0029] It is judged whether the voltage amplitude of the target node exceeds the safety threshold, and if the voltage amplitude of the target node exceeds the safety threshold, the target node is determined as an out-of-limit node;

[0030] According to the distribution network topology structure, adjacent nodes directly electrically connected with the out-of-limit node are identified;

[0031] Based on historical data and real-time load fluctuation, the probability of voltage out-of-limit of the adjacent nodes is predicted.

[0032] By adopting the technical scheme, based on the transformer area topology structure and the initial reactive power compensation demand of the nodes, the voltage amplitude of each node is solved by Newton-Raphson method iteration, and the mathematical rigor and convergence of the calculation result are ensured. The method is suitable for a nonlinear power system, can accurately reflect the influence of reactive power compensation on the voltage distribution of the power grid, and provides reliable data basis for the out-of-limit detection. By setting a voltage amplitude safety threshold, the out-of-limit nodes are automatically identified, and the real-time monitoring of the out-of-limit risk is realized. The out-of-limit nodes are directly marked, avoiding the blindness of manual investigation, and improving the fault response speed and operation and maintenance efficiency. Based on the transformer area topology structure, the adjacent nodes directly connected with the out-of-limit nodes are quickly located, and the propagation path of the out-of-limit risk is determined. In combination with historical data (such as historical out-of-limit events and load fluctuation rules) and real-time load prediction, the probability of voltage out-of-limit of the adjacent nodes is quantified, and a decision basis is provided for preventive control.

[0033] Optionally, the generating a global optimal reactive power compensation strategy according to the multi-objective optimization function and the reactive power compensation demand of each node comprises:

[0034] The reactive power compensation device capacity constraint, the voltage safety constraint and the power balance constraint are integrated into the multi-objective optimization function, and a Pareto optimal solution set is generated by a non-dominated sorting genetic algorithm;

[0035] A fuzzy membership function is used to quantify the satisfaction degree of each solution in the Pareto optimal solution set on each target, and the comprehensive satisfaction degree of each solution is calculated by weighting;

[0036] The solution with the highest comprehensive satisfaction degree is selected as the global optimal solution, and the global optimal solution is mapped to the reactive power compensation amount of each node.

[0037] By adopting the technical scheme, the reactive power compensation device capacity constraint (avoiding overload), the voltage safety constraint (ensuring voltage quality) and the power balance constraint (maintaining power grid stability) are integrated into the multi-objective optimization function, and the optimization result is ensured to meet the engineering practice. The Pareto optimal solution set is generated by a non-dominated sorting genetic algorithm, and a balance is achieved among multiple targets (such as minimization of voltage deviation, minimization of light rejection rate and minimization of line loss), and secondary problems caused by single target optimization are avoided. A fuzzy membership function is used to quantify the satisfaction degree of each solution in the Pareto optimal solution set on each target (such as voltage qualification satisfaction degree and light rejection rate satisfaction degree), and the solution comparison problem under multi-target conflict is solved. The comprehensive satisfaction degree of each solution is calculated by weighting, the unified evaluation of multiple targets is realized, and the global optimality of the solution is ensured. The solution with the highest comprehensive satisfaction degree is selected as the global optimal solution, and the optimality of the strategy under the multi-target constraint is ensured. The global optimal solution is mapped to the reactive power compensation amount of each node (such as static var generator (SVG) compensation amount and capacitor bank switching amount), the transformation from the theoretical optimum to the actual execution is realized, and the strategy issuing of the transformer area reactive power optimization platform is supported.

[0038] Optionally, the step of decomposing the global optimal reactive power compensation strategy into switching instructions of each reactive power compensation device further comprises:

[0039] generating real-time reactive power output instructions of the static reactive power generator according to dynamic reactive power demand in the global optimal reactive power compensation strategy and real-time photovoltaic fluctuation;

[0040] generating grading switching instructions of the capacitor bank according to basic reactive power demand in the global optimal reactive power compensation strategy and peak-valley period of the load;

[0041] generating reactive power set value of the inverter according to residual reactive power capacity demand in the global optimal reactive power compensation strategy and voltage out-of-limit node;

[0042] generating action signal of the phase-change switch according to three-phase unbalance compensation demand in the global optimal reactive power compensation strategy by using a greedy algorithm;

[0043] downloading the real-time reactive power output instructions, the grading switching instructions, the reactive power set value and the action signal to each target device.

[0044] By adopting the technical scheme, based on the dynamic reactive power demand in the global optimal strategy and real-time photovoltaic fluctuation, a real-time reactive power output instruction of a static var generator is generated through model predictive control (MPC), millisecond-level dynamic response is realized, and voltage flicker and three-phase imbalance caused by photovoltaic fluctuation are effectively inhibited. The MPC combines future photovoltaic output prediction, adjusts the SVG output in advance, reduces compensation hysteresis, and improves the stability of the power grid. According to the basic reactive power demand in the global optimal strategy and the peak-valley period of the load, a fuzzy control is used to generate a step switching instruction of the capacitor bank, the device wear caused by frequent switching is avoided, and the periodic changes of the load are adapted. The fuzzy control quantifies the load fluctuation through the membership function, realizes the smooth transition of the capacitor bank switching, and reduces the voltage impact. Based on the residual reactive power capacity demand and the voltage out-of-limit node in the global optimal strategy, a droop control is used to generate a reactive power set value of the inverter, the reactive power output is quickly adjusted through the local voltage-reactive power characteristic curve of the inverter, and the voltage out-of-limit is inhibited. The droop control does not require centralized communication, supports autonomous response of the inverter, and improves the self-healing capability of the power grid in the communication interruption. According to the three-phase imbalance compensation demand in the global optimal strategy, a greedy algorithm is used to generate an action signal of the phase-changing switch, the number of switch actions is minimized as the target, the load phase sequence is quickly adjusted, and the three-phase current is balanced. The greedy algorithm realizes real-time generation of the action signal of the phase-changing switch through local optimal selection, reduces the calculation complexity, and adapts to the real-time control demand. The SVG real-time reactive power output instruction, the capacitor bank step switching instruction, the inverter reactive power set value, and the phase-changing switch action signal are sent to each target device, multi-device collaborative control is realized, and the reactive power compensation efficiency is improved. The global strategy decomposition and local execution are supported, centralized optimization and distributed response are considered, and the large-scale area reactive power optimization demand is adapted.

[0045] In a second aspect of the present application, a reactive power optimization system for a transformer area is provided, comprising an acquisition module, a calculation module, a strategy module and an execution module, wherein:

[0046] The acquisition module is configured to obtain first data of a photovoltaic inverter, second data of a user electric meter and third data of the transformer area, determine photovoltaic fluctuation characteristics from the first data, determine load characteristics from the second data, and determine voltage out-of-limit characteristics from the third data, wherein the photovoltaic fluctuation characteristics include photovoltaic output fluctuation rate, change slope and intra-day fluctuation amplitude, the load characteristics include three-phase imbalance degree and peak-valley difference, and the voltage out-of-limit characteristics include duration, amplitude and frequency of voltage out-of-limit;

[0047] The calculation module is configured to calculate reactive power compensation demand of each node according to the photovoltaic fluctuation characteristics, the load characteristics, the voltage out-of-limit characteristics and the topology structure of the transformer area;

[0048] a strategy module, configured to construct a multi-objective optimization function according to the voltage deviation, the light abandonment rate and the line loss, and generate a globally optimal reactive compensation strategy according to the multi-objective optimization function and the reactive compensation demand of each node;

[0049] an execution module, configured to decompose the globally optimal reactive compensation strategy into switching instructions of each reactive compensation device for execution.

[0050] In a third aspect of the present application, an electronic device is provided, comprising a processor, a memory, a user interface and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the transformer area reactive power optimization method according to any one of the preceding aspects.

[0051] In a fourth aspect of the present application, a computer readable storage medium is provided, which stores instructions, when the instructions are executed, the transformer area reactive power optimization method according to any one of the preceding aspects is performed.

[0052] In summary, the one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0053] 1. By using real-time data of photovoltaic inverters, user meters and transformer area monitoring systems, photovoltaic fluctuation characteristics, load characteristics and voltage out-of-limit characteristics are extracted to realize comprehensive perception of transformer area operation states. Quantitative indexes (such as fluctuation rate and peak-valley difference) provide a data basis for subsequent calculations and avoid the roughness of traditional empirical methods; real-time data drive feature updates to adapt to rapid changes in photovoltaic output and load demand;

[0054] 2. Combining photovoltaic fluctuation characteristics, load characteristics, voltage out-of-limit characteristics and transformer area topology, the reactive compensation demand of each node is calculated. Different nodes obtain differentiated compensation demands according to their own characteristics (such as large photovoltaic fluctuation and three-phase load imbalance) to avoid a one-size-fits-all strategy; the demand is adjusted through the transformer area topology to reduce transmission loss of reactive power in the power grid and improve efficiency;

[0055] 3. Taking voltage deviation, light abandonment rate and line loss as optimization objectives, a non-dominated sorting genetic algorithm is used to generate a Pareto optimal solution set, and a fuzzy membership function is used to quantify comprehensive satisfaction. A balance is achieved between voltage quality, new energy utilization rate and power grid loss to avoid secondary problems (such as light abandonment caused by line loss reduction) caused by single objective optimization; the solution with the highest comprehensive satisfaction is selected from the Pareto frontier to ensure the optimality of the strategy under multiple objectives;

[0056] 4. Decompose the global strategy into SVG (corresponding to dynamic reactive power), capacitor bank (corresponding to basic reactive power), inverter (corresponding to residual reactive power), and switching of phase-changing switch (corresponding to three-phase imbalance). Different devices perform complementary tasks according to their characteristics (such as fast response of SVG, step switching of capacitor bank) to improve overall compensation effect; through real-time algorithms such as model predictive control (corresponding to SVG) and fuzzy control (corresponding to capacitor bank), adapt to dynamic change scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 is a flow diagram of a reactive power optimization method for a transformer area disclosed by an embodiment of the present application;

[0058] Figure 2 is a module diagram of a reactive power optimization system disclosed by an embodiment of the present application;

[0059] Figure 3 is a structural diagram of an electronic device disclosed by an embodiment of the present application.

[0060] Explanation of reference signs: 201, acquisition module; 202, calculation module; 203, strategy module; 204, execution module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0061] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in conjunction with the drawings in the embodiments of the specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments.

[0062] In the description of the embodiments of the present application, the words such as "for example" or "for instance" are used to represent an example, illustration or description. Any embodiment or design scheme described as "for example" or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concept in a specific way.

[0063] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first", "second", etc. are used only for the purpose of description and should not be understood as indicating or implying relative importance or implying the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. The terms "include", "contain", "have" and their variants mean "include but not limited to", unless otherwise specifically emphasized.

[0064] The embodiment discloses a method for optimizing reactive power of a transformer area, Figure 1 is a flowchart of a method for optimizing reactive power of a transformer area disclosed by the embodiment of the present application, which is applied to a transformer area reactive power optimization platform, such as Figure 1 As shown in the figure, the method comprises the following steps:

[0065] S101, acquiring first data of a photovoltaic inverter, second data of a user electric meter and third data of a transformer area, determining photovoltaic fluctuation characteristics from the first data, determining load characteristics from the second data, and determining voltage out-of-limit characteristics from the third data, the photovoltaic fluctuation characteristics including photovoltaic output fluctuation rate, change slope and intra-day fluctuation amplitude, the load characteristics including three-phase unbalance degree and peak-valley difference, and the voltage out-of-limit characteristics including duration, amplitude and frequency of voltage out-of-limit;

[0066] S102, calculating reactive power compensation demand of each node according to the photovoltaic fluctuation characteristics, the load characteristics, the voltage out-of-limit characteristics and the transformer area topology structure;

[0067] S103, constructing a multi-objective optimization function according to voltage deviation, light rejection rate and line loss, and generating a globally optimal reactive power compensation strategy according to the multi-objective optimization function and the reactive power compensation demand of each node;

[0068] S104, decomposing the globally optimal reactive power compensation strategy into switching instructions of each reactive power compensation device for execution.

[0069] It should be noted that the original operation data is obtained from the photovoltaic inverter (first data), user meter (second data) and transformer area (third data). The instability of photovoltaic power is quantified by photovoltaic output fluctuation rate (reflecting short-term fluctuation intensity), change slope (reflecting output change speed) and daily fluctuation amplitude (reflecting all-day fluctuation range). The load characteristics on the user side are quantified by three-phase imbalance degree (reflecting the difference between phases) and peak-valley difference (reflecting the daily fluctuation of load). The voltage quality of the transformer area is quantified by the duration of voltage out-of-limit (reflecting the severity of out-of-limit), amplitude (reflecting the degree of out-of-limit) and frequency (reflecting the number of out-of-limit occurrences). Data support is provided for subsequent reactive power compensation demand calculation, and the abnormal characteristics of photovoltaic, load and voltage are clarified. Combining the photovoltaic fluctuation characteristics, load characteristics, voltage out-of-limit characteristics and transformer area topology (such as line impedance and node connection relationship), the reactive power compensation demand of each node is calculated. The greater the photovoltaic output fluctuation rate, the higher the node reactive power compensation demand (to suppress voltage flicker). The higher the three-phase imbalance degree, the higher the node reactive power compensation demand (to balance three-phase current). The higher the voltage out-of-limit frequency, the higher the node reactive power compensation demand (to maintain voltage stability). Through topology analysis, the transmission path of reactive power in the power grid is determined, and the deployment location of the compensation device is optimized. The preliminary reactive power compensation demand of each node is generated, providing input for global optimization. The objective function includes voltage deviation minimization, light rejection rate minimization and line loss minimization. Voltage deviation minimization: reduces the deviation of node voltage from the rated value, improving voltage quality. Light rejection rate minimization: reduces photovoltaic power limiting due to voltage out-of-limit, improving new energy utilization. Line loss minimization: optimizes reactive power distribution, reducing power grid transmission loss. The constraint conditions include: reactive power compensation device capacity constraint, voltage safety constraint and power balance constraint. The non-dominated sorting genetic algorithm (NSGA-II) is used to generate a Pareto optimal solution set, balancing the multi-objective conflict. Fuzzy satisfaction weighted: quantifies the satisfaction of each solution on each target, and selects the solution with the highest comprehensive satisfaction as the global optimal solution. A global optimal reactive power compensation strategy is generated, which takes into account voltage quality, new energy utilization and power grid loss. The global optimal reactive power compensation strategy is decomposed into specific control instructions for each reactive power compensation device (such as SVG (static var generator), capacitor bank, inverter and commutating switch). SVG real-time reactive power output instruction: based on model predictive control (MPC), responding to dynamic reactive power demand and photovoltaic fluctuation. Capacitor bank step switching instruction: based on fuzzy control, adapting to load peak and valley periods and basic reactive power demand. Inverter reactive power set value: based on droop control, responding to remaining reactive power capacity demand and voltage out-of-limit nodes. Commutating switch action signal: based on the greedy algorithm, optimizing three-phase imbalance compensation demand. Instruction issuance and execution: the control instructions are issued to each target device to realize real-time execution of the reactive power compensation strategy. The global optimal strategy is converted into device-level control instructions, improving the adaptability and control accuracy of the power grid to complex working conditions.

[0070] In one embodiment, S102, calculating the reactive power compensation demand of each node according to the photovoltaic fluctuation characteristics, load characteristics, voltage out-of-limit characteristics and transformer topology structure comprises:

[0071] S1021, forming a state description of each node according to the photovoltaic fluctuation characteristics, load characteristics, voltage out-of-limit characteristics and transformer topology structure;

[0072] S1022, calculating the preliminary reactive power compensation demand of each node according to the state description, and adjusting the preliminary reactive power compensation demand of each node according to the transformer topology structure;

[0073] S1023, weighting and summing the adjusted photovoltaic fluctuation compensation demand, three-phase imbalance compensation demand and voltage out-of-limit compensation demand to obtain the reactive power compensation demand of each node.

[0074] It should be noted that the output fluctuation rate (such as the power change rate within 5 minutes > 20%): reflects the short-term instability of photovoltaic power. The intra-day fluctuation range (such as the difference between the maximum power and the minimum power): reflects the power change range throughout the day. The three-phase imbalance degree (such as the ratio of the maximum phase current to the minimum phase current > 1.2): reflects the difference between the phases of the load. The peak-valley difference (such as the difference between the maximum value and the minimum value of the daily load): reflects the intensity of the daily fluctuation of the load. The voltage out-of-limit duration (such as the cumulative time when the voltage > 1.1 p.u. or < 0.9 p.u.): reflects the severity of the out-of-limit. The out-of-limit amplitude (such as the absolute value of the voltage deviation): reflects the out-of-limit degree. The line impedance (such as R+jX parameters): affects the transmission loss of the reactive power. The node connection relationship (such as radial / circular network): determines the deployment location of the reactive power compensation device. Quantify the above characteristics into a numerical vector (such as [fluctuation rate, imbalance degree, out-of-limit amplitude, line impedance]), forming a state description matrix of the node. According to the node state description, the preliminary reactive power compensation demand of each node is calculated, and the influence of each characteristic on the reactive power compensation is quantified.

[0075] The photovoltaic fluctuation compensation demand is calculated by the following formula: Q_pv=k_pv* (fluctuation rate + intra-day fluctuation range), wherein k_pv is the photovoltaic compensation coefficient (related to the photovoltaic installed capacity). The voltage flicker and three-phase imbalance caused by the sudden change of photovoltaic output are suppressed.

[0076] The three-phase imbalance compensation demand is calculated by the following formula: Q_unb=k_unb*imbalance degree, wherein k_unb is the imbalance compensation coefficient (related to the load type). The three-phase current is balanced by reactive power compensation, and the neutral line current is reduced.

[0077] The voltage out-of-limit compensation demand is calculated by the following formula: Q_volt=k_volt* (out-of-limit amplitude + out-of-limit frequency), wherein k_volt is the voltage compensation coefficient (related to the voltage grade). The reactive power is adjusted to pull the node voltage back to the safe range.

[0078] Get the preliminary reactive power compensation demand vector of each node (such as [Q_pv, Q_unb, Q_volt]).

[0079] According to the substation topology (such as line impedance, node connection relationship), adjust the preliminary reactive power compensation demand of each node, and optimize the distribution path of reactive power.

[0080] The impedance weighted adjustment can be carried out by the following formula: Q_adj=Q_init / (1+α*R), where Q_init is the preliminary demand, R is the line resistance, and α is the impedance weight coefficient. Reduce the transmission loss of reactive power on long lines, and preferentially compensate high impedance nodes.

[0081] Determine the optimal transmission path of reactive power by the shortest path algorithm (such as Dijkstra algorithm), avoid reactive power circulation. Ensure that the reactive power compensation device is deployed at the key node where the voltage is out of limit or the photovoltaic fluctuation is concentrated. Get the reactive power compensation demand vector after topology adjustment (such as [Q_pv_adj, Q_unb_adj, Q_volt_adj]). Weighted sum of adjusted photovoltaic fluctuation compensation demand, three-phase unbalance compensation demand, voltage out-of-limit compensation demand, get the comprehensive reactive power compensation demand of each node. Weight distribution:

[0082] Photovoltaic fluctuation weight (w_pv): determined according to photovoltaic penetration rate (such as photovoltaic proportion> 30%, w_pv=0.5). Three-phase unbalance weight (w_unb): determined according to load type (such as high industrial load proportion, w_unb=0.3). Voltage out-of-limit weight (w_volt): determined according to voltage out-of-limit frequency (such as out-of-limit frequency> 5 times / day, w_volt=0.4).

[0083] The final reactive power compensation demand Q_total of each node can be calculated by the following formula:

[0084] Q_total=w_pv*Q_pv_adj+w_unb*Q_unb_adj+w_volt*Q_volt_adj

[0085] Get the final reactive power compensation demand of each node (such as Q_total=120kvar), which is used for subsequent strategy generation.

[0086] The comprehensive state description of each node is formed by combining the photovoltaic fluctuation characteristics, load characteristics, voltage out-of-limit characteristics, and substation topology structure, so as to avoid the calculation deviation caused by a single characteristic. The preliminary reactive power compensation demand of each node is calculated based on the state description, so as to ensure that the demand is strongly related to the actual operation state and improve the accuracy of the compensation demand. The preliminary reactive power compensation demand of each node is adjusted according to the substation topology structure, the transmission path of the reactive power in the power grid (such as the line impedance affecting the compensation effect) is considered, and the overcompensation or undercompensation caused by unreasonable topology is avoided. Through the topology structure analysis, the compensation demand distribution of adjacent nodes (such as the compensation linkage of the voltage out-of-limit node and the adjacent node) is optimized, the efficiency of the whole network reactive power compensation is improved, and the redundant compensation is reduced. The adjusted photovoltaic fluctuation compensation demand, three-phase imbalance compensation demand, and voltage out-of-limit compensation demand are weighted and summed, the weight is dynamically allocated according to the priority of each demand (such as the voltage out-of-limit priority is higher than the photovoltaic fluctuation), and the comprehensive optimization of multiple targets is realized. The weighting coefficient can be dynamically adjusted according to the actual operation demand of the substation (such as the new energy proportion and the load characteristics), the demand customization in different scenes is supported, and the adaptability of the compensation strategy is improved.

[0087] In one embodiment, S1021, forming a state description of each node according to photovoltaic fluctuation characteristics, load characteristics, voltage out-of-limit characteristics, and substation topology structure comprises:

[0088] S10211, mapping the photovoltaic fluctuation characteristics to the photovoltaic grid-connected node, mapping the load characteristics to the user load node, and mapping the voltage out-of-limit characteristics to the voltage out-of-limit node to determine the characteristic description of each node;

[0089] S10212, determining the electrical connection relationship of each node with other nodes according to the substation topology structure, and calculating the reactive power transmission loss between nodes;

[0090] S10213, determining the state description of each node according to the characteristic description, the electrical connection relationship, and the reactive power transmission loss.

[0091] It needs to be noted that the photovoltaic fluctuation characteristics (such as photovoltaic output fluctuation rate, change slope, daily fluctuation amplitude) are mapped to the photovoltaic grid-connected node. The influence position of photovoltaic fluctuation on the power grid is determined, for example, the photovoltaic node with high fluctuation rate needs stronger reactive power compensation capability to suppress voltage flicker. The load characteristics (such as three-phase imbalance degree, peak-valley difference) are mapped to the user load node. The node with abnormal load characteristics is located, for example, the node with high three-phase imbalance degree needs three-phase imbalance compensation (such as phase change switch action). The voltage out-of-limit characteristics (such as out-of-limit duration, amplitude, frequency) are mapped to the voltage out-of-limit node. The node with poor voltage quality is identified, for example, the node with large out-of-limit amplitude needs to preferentially increase reactive power compensation to improve the voltage level. Through feature mapping, the feature description of each node is formed, and the specific problems of each node in photovoltaic fluctuation, load characteristics, and voltage quality are determined. According to the substation topology (such as line connection diagram, node position), the electrical connection relationship of each node with other nodes is determined, for example, node A is connected with node B through line L1. The node adjacency matrix is established, and the upstream node, downstream node, and adjacent node of each node are determined, which provides a basis for reactive power transmission analysis. Based on the line impedance (resistance R, reactance X) and node voltage, the reactive power transmission loss (ΔQ = I 2 X, where I is the line current) between nodes is calculated. The reactive power transmission loss reduces the compensation effect, for example, the reactive power compensation of node A may not be completely transmitted to node B due to line loss, and additional compensation is needed at node B. The electrical connection relationship and reactive power transmission loss between nodes are determined, which provides a topology constraint for subsequent state description. Feature description: integrate photovoltaic fluctuation characteristics, load characteristics, and voltage out-of-limit characteristics to quantify the operating state of the node (such as photovoltaic fluctuation intensity, load imbalance degree, and voltage out-of-limit severity). Electrical connection relationship: describes the position of the node in the power grid and the connection mode with other nodes (such as radial, ring), which affects the transmission path of reactive power. Reactive power transmission loss: quantifies the loss of reactive power transmission between nodes, reflecting the attenuation degree of compensation effect. The state description of node i can be represented as Si = {Fi, Ci, Li}, where: Fi represents the feature description (photovoltaic, load, and voltage out-of-limit characteristics), Ci represents the electrical connection relationship (adjacent nodes, line impedance), and Li represents the reactive power transmission loss (loss between node i and adjacent nodes). Through weighted or fuzzy comprehensive evaluation, multi-dimensional information is fused into the state score of the node (such as 0-100 points), and the higher the score, the worse the node state, and the more priority compensation is needed. The state description of each node is generated, which provides comprehensive and quantitative input for subsequent reactive power compensation demand calculation.

[0092] Suppose a certain substation contains the following nodes:

[0093] Node 1: photovoltaic grid-connected node, photovoltaic fluctuation rate 20% (high fluctuation).

[0094] Node 2: User load node, three-phase imbalance 15% (high imbalance).

[0095] Node 3: Voltage out-of-limit node, out-of-limit amplitude 5% (moderate out-of-limit).

[0096] Topology: Node 1→Node 2→Node 3 (series connection), line impedance 0.5Ω, 0.3Ω respectively.

[0097] Feature description:

[0098] Node 1: Photovoltaic fluctuation rate 20%. Node 2: Three-phase imbalance 15%. Node 3: Voltage out-of-limit amplitude 5%.

[0099] Electrical connection relationship:

[0100] Node 1 is connected to Node 2 with a line impedance of 0.5Ω; Node 2 is connected to Node 3 with a line impedance of 0.3Ω.

[0101] Reactive power transmission loss:

[0102] Node 1→Node 2: Assuming current 10A, loss ΔQ12=10 2 ×0.5=50Var.

[0103] Node 2→Node 3: loss ΔQ23=10 2 ×0.3=30Var.

[0104] State description:

[0105] Node 1: S1={20%, Node 1→Node 2, 50Var}.

[0106] Node 2: S2={15%, Node 1←→Node 3, 80Var} (total loss).

[0107] Node 3: S3={5%, Node 2→Node 3, 30Var}.

[0108] Through the state description, it is clear that Node 1 needs to prioritize compensation for photovoltaic fluctuations, Node 2 needs to compensate for three-phase imbalance, and Node 3 needs to compensate for voltage out-of-limit, while considering the impact of line loss on compensation effect.

[0109] The photovoltaic fluctuation characteristics (such as volatility, slope, amplitude) are mapped to the photovoltaic grid-connected node, the load characteristics (such as three-phase imbalance, peak-valley difference) are mapped to the user load node, and the voltage out-of-limit characteristics (such as duration, amplitude, frequency) are mapped to the voltage out-of-limit node, so as to ensure that the characteristic description of each node is strongly related to the actual running state. Through the characteristic mapping, a multi-dimensional characteristic description (such as photovoltaic fluctuation intensity, load imbalance degree, voltage out-of-limit risk) of each node is formed, which provides accurate input for subsequent demand calculation. According to the distribution area topology structure, the electrical connection relationship (such as direct connection, connection through line impedance) of each node with other nodes is determined, which provides a basis for the analysis of reactive power transmission path. Based on the topology structure and line impedance, the reactive power transmission loss between nodes is calculated, and the influence of reactive power compensation on adjacent nodes (such as voltage fluctuation propagation caused by compensation device switching) is quantified, so as to avoid the failure of compensation strategy due to the neglect of loss. The characteristic description (photovoltaic fluctuation, load imbalance, voltage out-of-limit), electrical connection relationship (node topology), and reactive power transmission loss are comprehensively described as the state description of each node, forming a complete model covering the running state, topology constraint, and loss influence. The comprehensive state description provides a global perspective for subsequent reactive power compensation demand calculation (such as preliminary demand quantification based on state description, topology adjustment), and supports the generation of dynamic optimization strategy (such as adjusting the compensation device position according to the loss).

[0110] In one embodiment, S1022, the preliminary reactive power compensation demand of each node is calculated according to the state description, and the preliminary reactive power compensation demand of each node is adjusted according to the distribution area topology structure, including:

[0111] S10221, according to the mapping relationship between characteristics and reactive power compensation demand, the basic compensation demand of each characteristic in each node is calculated, and the weighted sum of multiple basic compensation demands is obtained to obtain the preliminary reactive power compensation demand of each node;

[0112] S10222, using a power flow calculation method to simulate the grid state after reactive power compensation, the reactive power distribution results and adjacent node voltage out-of-limit information of each node are calculated;

[0113] S10223, the preliminary reactive power compensation demand of each node is adjusted according to the adjacent node voltage out-of-limit information.

[0114] It should be noted that according to the mapping relationship of photovoltaic fluctuation characteristics, load characteristics, voltage out-of-limit characteristics and reactive power compensation demand (such as the positive correlation between photovoltaic fluctuation rate and dynamic reactive power compensation amount, and the positive correlation between three-phase unbalance degree and reactive power balancing amount), the basic compensation demand of each characteristic in each node is calculated. The multiple basic compensation demands (such as photovoltaic fluctuation compensation demand, three-phase unbalance compensation demand, and voltage out-of-limit compensation demand) are weighted and summed according to the weight to obtain the preliminary reactive power compensation demand of each node. The weight can be dynamically adjusted according to the priority of the characteristics (such as the priority of voltage out-of-limit being higher than that of photovoltaic fluctuation), or obtained through historical data training. The preliminary demand quantification driven by multiple characteristics is realized, and a benchmark is provided for subsequent adjustment. The weighted sum mechanism supports multi-objective comprehensive optimization, avoiding overcompensation or undercompensation of a single characteristic. Using the Newton-Raphson method, the forward-backward substitution method and other power flow calculation methods, the state of the power grid after reactive power compensation (such as node voltage and branch power) is simulated, and the reactive power distribution results of each node and the voltage out-of-limit information of adjacent nodes are calculated. The reactive power distribution of each node after compensation is quantified, and the compensation effect is verified. It is identified whether the voltage of adjacent nodes after compensation is out of limit (such as excessively high or low voltage), and the duration, amplitude and frequency of the out-of-limit are analyzed. The rationality of the preliminary compensation demand is verified, and the potential problems of the power grid after compensation (such as voltage out-of-limit propagation) are revealed. Data support is provided for demand adjustment to ensure that the compensation strategy will not cause new operation risks. According to the voltage out-of-limit information of adjacent nodes, it is evaluated whether the preliminary reactive power compensation demand of each node will cause voltage out-of-limit (such as excessively large compensation amount leading to excessively high voltage of adjacent nodes). If the voltage of adjacent nodes is out of limit after compensation, the reactive power compensation amount of the current node is reduced (such as reducing the output of SVG or reducing the switching of capacitor banks). If the compensation of the current node causes the voltage of adjacent nodes to be out of limit, the compensation demand of the adjacent nodes is adjusted (such as increasing the switching of reactive power absorbing devices of adjacent nodes). Through iterative adjustment, the compensation demand is gradually optimized until the voltage safety constraint is met. The voltage out-of-limit risk after compensation is eliminated to ensure the safe operation of the power grid. The dynamic optimization of compensation demand is realized, and the voltage quality and reactive power compensation effect are balanced.

[0115] By mapping the characteristics (such as photovoltaic volatility, load imbalance, voltage out-of-limit amplitude) to the reactive power compensation demand, the basic compensation demand of each characteristic is quantified. The weighted sum (weights are dynamically adjusted according to the importance of the characteristics) is used to generate the preliminary reactive power compensation demand of the node, ensuring that the demand is strongly related to the actual operating state. Avoiding the calculation bias caused by a single characteristic, the scientific nature of demand quantification is improved. The weights are adjustable, supporting demand customization in different scenarios (such as high photovoltaic penetration area). Through power flow calculation, the state of the power grid after reactive power compensation is simulated, and the reactive power distribution results of each node (such as the reactive power injection of the node after compensation, the line reactive power flow) are quantified. The voltage out-of-limit information of adjacent nodes (such as the voltage of a certain node exceeding the upper limit or being lower than the lower limit after compensation) is identified, providing a basis for demand adjustment. The actual power grid response after compensation is simulated, avoiding the failure of the strategy caused by static calculation. The impact of compensation on the whole network (such as overcompensation of a certain node leading to voltage out-of-limit of adjacent nodes) is revealed, supporting collaborative optimization. According to the voltage out-of-limit information of adjacent nodes (such as the out-of-limit amplitude, duration), the preliminary reactive power compensation demand of each node is dynamically adjusted. For example, if the voltage of a certain node is out-of-limit after compensation, the compensation demand of the node or adjacent nodes is reduced. Iterative optimization (such as multiple power flow calculations and demand adjustments) is used until the voltage safety constraint is met, ensuring the feasibility of the compensation strategy. Avoiding voltage out-of-limit caused by compensation strategy, ensuring stable operation of the power grid. Adapt to complex power grid working conditions (such as multi-node coupling, dynamic load change), improve the reliability of the strategy.

[0116] In one embodiment, in S10222, the calculation of the reactive power distribution results of each node and the voltage out-of-limit information of adjacent nodes includes:

[0117] S102221, taking the area topology and the initial reactive power compensation demand of each node as input, the voltage amplitude of each node is iteratively solved by Newton-Raphson method;

[0118] S102222, judging whether the voltage amplitude of the target node exceeds the safety threshold, if the voltage amplitude of the target node exceeds the safety threshold, the target node is determined as the out-of-limit node;

[0119] S102223, according to the area topology, the adjacent nodes directly connected with the out-of-limit node are identified;

[0120] S102224, based on historical data and real-time load fluctuation, the probability of voltage out-of-limit of adjacent nodes is predicted.

[0121] It should be noted that the power flow calculation model of the power grid is constructed using the transformer substation topology (such as node connection relationships and line impedance parameters) and the initial reactive power compensation requirements of each node (such as SVG reactive power output value and capacitor bank switching status) as inputs. The Newton-Raphson method is used to iteratively solve the nonlinear power flow equations to calculate the voltage amplitude and phase angle of each node. The Newton-Raphson method iteratively corrects node voltages through the Jacobian matrix, exhibiting second-order convergence characteristics and is suitable for accurate calculations of large-scale power grids. The voltage amplitude of each node (such as the actual value) is obtained and used for subsequent voltage limit judgment. According to power grid operation standards, a safe threshold for voltage amplitude is set (such as ±7% or ±10% of the rated voltage). The voltage amplitudes of all nodes are iterated to determine if any target node's voltage amplitude exceeds the safe threshold (such as voltage >1.07 pu or <0.93 pu). If so, the target node is determined as an over-limit node, and the over-limit type (such as voltage too high or too low) and over-limit magnitude (such as the percentage exceeding the threshold) are recorded. Generate a list of nodes exceeding voltage limits, including node number, exceeding type, and exceeding magnitude. Based on the transformer substation topology (e.g., adjacency matrix or node-branch association table), identify adjacent nodes directly electrically connected to the exceeding node. Adjacent nodes refer to nodes directly connected via lines or transformers, whose voltage is significantly affected by the exceeding node (e.g., through line impedance coupling). Generate a list of adjacent nodes for the exceeding node, providing input for subsequent risk prediction. Based on historical load data (e.g., peak and valley periods, load fluctuation rate) and voltage exceeding records, construct a statistical model (e.g., Markov chain model) for adjacent node voltage exceeding, quantifying historical exceeding probabilities. Combining real-time load fluctuations (e.g., photovoltaic output fluctuations, sudden changes in user load) and meteorological data (e.g., temperature, solar intensity), use time series forecasting methods (e.g., ARIMA, LSTM) to predict future short-term load changes. Construct a classification model based on support vector machines or random forests, inputting historical exceeding features (e.g., load fluctuation rate, voltage amplitude of exceeding node) and real-time load prediction results, outputting the probability of adjacent node voltage exceeding (e.g., probability value between 0 and 1). The probability Pexceeds of voltage exceeding the limit can be calculated using the following formula:

[0122] P_overlimit = f(ΔP_load, ΔV_overlimit_node, T_environment)

[0123] Where ΔPload represents the load fluctuation rate, ΔVover-limit node represents the voltage change at the over-limit node, and Tenvironment represents the ambient temperature. A list of voltage over-limit probabilities for adjacent nodes is generated to guide the dynamic adjustment of reactive power compensation strategies.

[0124] With the transformer topology (line impedance, node connection relationship) and the initial reactive power compensation demand of each node as input, the voltage amplitude of each node is solved by Newton-Raphson method iteration. This method can quickly converge to the steady-state solution and accurately reflect the voltage distribution of the compensated power grid. Quantify the voltage amplitude of each node to provide basic data for voltage out-of-limit detection. Newton-Raphson method is suitable for solving nonlinear equations and adapting to complex power grid conditions (such as multi-node coupling and dynamic load changes). The iterative solution process is efficient and suitable for real-time or quasi-real-time analysis scenarios. Clearly identify the location and out-of-limit degree (such as out-of-limit amplitude and duration) of the out-of-limit node to provide a basis for subsequent risk analysis and strategy adjustment. Quickly detect voltage out-of-limit to support the generation of dynamic optimization strategies. According to the transformer topology, identify the adjacent nodes directly connected to the out-of-limit node (such as nodes connected through line impedance). Clearly define the scope of adjacent nodes to provide a basis for risk propagation analysis. Based on historical data (such as historical voltage out-of-limit records and load fluctuation patterns) and real-time load fluctuation (such as photovoltaic output prediction and user load prediction), predict the probability of voltage out-of-limit of adjacent nodes. Use machine learning algorithms (such as time series prediction and probability models) to quantify the out-of-limit risk and support preventive control. Identify potential out-of-limit risks in advance to avoid the cascading effect caused by compensation strategies. Combine historical data with real-time prediction to improve the accuracy of risk prediction.

[0125] In one embodiment, in S103: generating a globally optimal reactive power compensation strategy according to the multi-objective optimization function and the reactive power compensation demand of each node includes:

[0126] S1031, incorporate reactive power compensation device capacity constraints, voltage safety constraints and power balance constraints in the multi-objective optimization function, and generate a Pareto optimal solution set by non-dominated sorting genetic algorithm;

[0127] S1032, use fuzzy membership function to quantify the satisfaction degree of each solution in the Pareto optimal solution set on each target, and calculate the comprehensive satisfaction degree of each solution by weighting;

[0128] S1033, select the solution with the highest comprehensive satisfaction degree as the globally optimal solution, and map the globally optimal solution to the reactive power compensation amount of each node.

[0129] It should be noted that multiple optimization objectives (such as minimizing voltage risk, minimizing reactive power compensation cost, and maximizing new energy consumption capacity) are defined to form a multi-objective optimization function. The constraint conditions are integrated into the objective function, including reactive power compensation device capacity constraints, voltage safety constraints, and power balance constraints. Reactive power compensation device capacity constraints: ensure that the compensation amount of each node does not exceed the rated capacity of the device (such as the maximum switching capacity of the capacitor bank). Voltage safety constraints: the node voltage amplitude needs to be within the safety threshold range (such as 0.95~1.05 pu). Power balance constraints: the total network reactive power needs to meet the balance equation. The NSGA-II generates a Pareto optimal solution set (each solution in the solution set is better in one objective, but not worse in other objectives). NSGA-II uses fast non-dominated sorting and congestion distance calculation to ensure solution set diversity and convergence. For each target (such as voltage risk and compensation cost), define a fuzzy membership function to map the target value to a satisfaction degree (between 0 and 1). According to the importance of each target (such as voltage safety weight 0.6, cost weight 0.3, and new energy consumption weight 0.1), calculate the comprehensive satisfaction degree of each solution. Select the solution with the highest comprehensive satisfaction degree from the Pareto optimal solution set as the global optimal solution. For example, if the comprehensive satisfaction degree of solution A is 0.85 and that of solution B is 0.78, select solution A. Map the global optimal solution to the reactive power compensation amount of each node. For example, if solution A corresponds to a compensation amount of 50kVar for node 1 and 30kVar for node 2, generate a specific compensation strategy:

[0130] x optimal =[50,30,…] T

[0131] Verify the global optimal solution through power flow calculation to ensure that all constraint conditions are met (such as whether the voltage amplitude is within the safety range), ensuring the feasibility of the strategy.

[0132] In the multi-objective optimization function, the capacity constraint of reactive power compensation device (avoiding overload), the voltage safety constraint (ensuring voltage quality), and the power balance constraint (maintaining grid stability) are integrated to ensure that the optimization result conforms to the engineering practice. The non-dominated sorting genetic algorithm is used to generate a Pareto optimal solution set, and a balance is achieved among multiple objectives (such as minimization of voltage deviation, minimization of light rejection rate, and minimization of line loss) to avoid secondary problems caused by single-objective optimization. The fuzzy membership function is used to quantify the satisfaction degree of each solution in the Pareto solution set on each objective (such as voltage qualified rate satisfaction degree and light rejection rate satisfaction degree) to solve the solution comparison problem under multi-objective conflict. The comprehensive satisfaction degree of each solution is calculated by weighting to realize the unified evaluation of multiple objectives and ensure the global optimality of the solution. The solution with the highest comprehensive satisfaction degree is selected as the global optimal solution to ensure the optimality of the strategy under multi-objective constraints. The global optimal solution is mapped to the reactive power compensation amount of each node (such as SVG compensation amount and capacitor bank switching amount) to realize the transformation from the theoretical optimum to the actual execution and support the strategy issuance of the distribution area reactive power optimization platform.

[0133] In one embodiment, S104, decomposing the global optimal reactive power compensation strategy into switching instructions of each reactive power compensation device for execution further includes:

[0134] S1041, generating real-time reactive power output instructions of the static reactive power generator by using model predictive control according to the dynamic reactive power demand in the global optimal reactive power compensation strategy and real-time photovoltaic fluctuation;

[0135] S1042, generating grading switching instructions of the capacitor bank by using fuzzy control according to the basic reactive power demand in the global optimal reactive power compensation strategy and the peak-valley period of load;

[0136] S1043, generating the reactive power set value of the inverter by using droop control according to the residual reactive power capacity demand in the global optimal reactive power compensation strategy and the voltage out-of-limit node;

[0137] S1044, generating the action signal of the phase-change switch by using the greedy algorithm according to the three-phase unbalance compensation demand in the global optimal reactive power compensation strategy;

[0138] S1045, issuing the real-time reactive power output instructions, grading switching instructions, reactive power set value, and action signal to each target device.

[0139] It should be noted that the global optimal strategy includes dynamic reactive power demand (such as responding to rapid fluctuations in photovoltaic output), and real-time photovoltaic fluctuations are obtained through real-time monitoring systems (such as triggering control when photovoltaic power changes by more than 5%). Model predictive control (MPC) is used to predict the reactive power demand for a certain period of time (such as 10 seconds) in the future, and generate real-time reactive power output instructions for SVG. MPC ensures that the SVG output matches the dynamic demand through rolling optimization and feedback correction. The global optimal strategy includes basic reactive power demand (such as reactive power compensation demand during load peak and valley periods), and load peak and valley periods are divided through load prediction or historical data (such as peak period 7:00-22:00, valley period 22:00-7:00). Fuzzy control is used to generate capacitor bank switching instructions. The fuzzy control input is voltage deviation and reactive power demand deviation, and the output is the capacitor bank switching position (such as 0, 1, 2). For example, if the voltage deviation is "positive large" and the reactive power demand deviation is "positive large", the capacitor bank switching position is "+2". The barycenter method is used to convert the fuzzy output into specific position instructions. The global optimal strategy includes residual reactive power capacity demand (such as the residual reactive power demand after SVG and capacitor bank compensation), and voltage out-of-limit nodes are identified through voltage monitoring systems (such as voltage amplitude exceeding 1.05 pu or below 0.95 pu). Droop control is used to generate the reactive power set value of the inverter. Droop control adjusts the inverter output through the voltage-reactive power droop curve (such as )), where Q0 is the initial reactive power output, k is the droop coefficient, V0 is the reference voltage, and V represents the voltage amplitude of the inverter access point. The droop coefficient is adjusted according to the voltage out-of-limit degree (such as k=10 kVar / pu when the voltage out-of-limit is 0.05 pu). The inverter reactive power output needs to meet the capacity constraint (such as , Qinv represents the reactive power output of the inverter). The global optimal strategy includes three-phase imbalance compensation demand (such as triggering control when the three-phase current imbalance exceeds 15%). The greedy algorithm is used to generate the action signal of the phase change switch. The greedy algorithm selects the current optimal phase change action each time (such as switching the largest load phase to the smallest load phase), gradually reducing the three-phase imbalance. For example, if the A-phase current is the largest and the C-phase current is the smallest, then switch part of the A-phase load to the C-phase. Stop when the three-phase imbalance is less than 5%.

[0140] Model predictive control (MPC) can quickly respond to photovoltaic power fluctuations (such as second-level fluctuations) through rolling optimization and real-time feedback, significantly improving the adaptability of the power grid to new energy fluctuations. MPC minimizes reactive power output errors and control variable changes, reducing frequent adjustments of SVGs, extending equipment life, and reducing reactive power compensation losses. MPC can handle multiple variable constraints (such as SVG capacity limits and voltage safety constraints), ensuring control accuracy and stability in complex conditions. Fuzzy control reduces the number of capacitor bank switches (such as a 30% reduction in switching frequency) by fuzzifying voltage deviation and reactive power demand deviation, extending equipment life and reducing operational costs. Fuzzy control can automatically adjust the switching position according to load peak and valley periods without human intervention, adapting to dynamic changes in load and improving the flexibility of reactive power compensation. In areas with large load peak-to-valley differences, fuzzy control of the capacitor bank can balance the basic reactive power demand and reduce voltage fluctuations. Droop control achieves distributed reactive power regulation of inverters through a voltage-reactive power droop curve, eliminating the need for centralized communication, reducing system complexity, and improving response speed (response time < 1 second). Droop control can quickly respond to voltage limit nodes by adjusting the reactive power output of inverters (such as absorbing reactive power when the voltage rises and emitting reactive power when the voltage drops), restoring the voltage amplitude to a safe range (such as within ±5%). Greedy algorithm quickly reduces three-phase unbalance (such as from 15% to less than 5%) by selecting the current optimal commutation action each time (such as switching the maximum load phase to the minimum load phase), improving power quality. The greedy algorithm does not require global information and only optimizes locally based on the current unbalance, reducing computational complexity and being suitable for real-time control. Three-phase imbalance can cause equipment overheating and increased losses, and the commutation switch controlled by the greedy algorithm can effectively reduce the unbalance, extending the life of the equipment. By decomposing the global optimal reactive power compensation strategy into specific instructions and issuing them to each device, automation and intelligentization of reactive power compensation are achieved, reducing human intervention and improving power grid operation efficiency. Different control methods (MPC, fuzzy control, droop control, and greedy algorithm) are customized for different devices (SVG, capacitor bank, inverter, and commutation switch) based on their characteristics, achieving collaborative control and improving overall control effectiveness. After issuing the instructions, the monitoring system verifies the execution effect (such as whether the voltage amplitude, reactive power, and three-phase unbalance meet the requirements), and if not, the instructions are regenerated, forming a closed-loop control to ensure the effective implementation of the strategy.

[0141] The embodiment of the present application also discloses a kind of reactive power optimization system of area, Figure 2 It is the module schematic diagram of a kind of area reactive power optimization system disclosed in the embodiment of the present application, as Figure 2 As shown, the system includes acquisition module 201, calculation module 202, strategy module 203 and execution module 204, wherein:

[0142] The collection module 201 is configured to acquire first data of a photovoltaic inverter, second data of a user electricity meter and third data of a transformer area, determine photovoltaic fluctuation characteristics from the first data, determine load characteristics from the second data, and determine voltage out-of-limit characteristics from the third data, the photovoltaic fluctuation characteristics including photovoltaic output fluctuation rate, change slope and intra-day fluctuation amplitude, the load characteristics including three-phase unbalance degree and peak-valley difference, and the voltage out-of-limit characteristics including voltage out-of-limit duration, amplitude and frequency;

[0143] The calculation module 202 is configured to calculate reactive power compensation demand of each node according to the photovoltaic fluctuation characteristics, the load characteristics, the voltage out-of-limit characteristics and transformer area topology structure;

[0144] The strategy module 203 is configured to construct a multi-objective optimization function according to voltage deviation, light rejection rate and line loss, and generate a global optimal reactive power compensation strategy according to the multi-objective optimization function and the reactive power compensation demand of each node;

[0145] The execution module 204 is configured to decompose the global optimal reactive power compensation strategy into switching instructions of each reactive power compensation device for execution.

[0146] In one embodiment, the calculation module 202 is configured to:

[0147] Form a state description of each node according to the photovoltaic fluctuation characteristics, the load characteristics, the voltage out-of-limit characteristics and the transformer area topology structure;

[0148] Calculate preliminary reactive power compensation demand of each node according to the state description, and adjust the preliminary reactive power compensation demand of each node according to the transformer area topology structure;

[0149] Weighted sum the adjusted photovoltaic fluctuation compensation demand, three-phase unbalance compensation demand and voltage out-of-limit compensation demand to obtain the reactive power compensation demand of each node.

[0150] In one embodiment, the calculation module 202 is configured to:

[0151] Map the photovoltaic fluctuation characteristics to photovoltaic grid-connected nodes, map the load characteristics to user load nodes, and map the voltage out-of-limit characteristics to voltage out-of-limit nodes to determine a characteristic description of each node;

[0152] Determine electrical connection relationship of each node with other nodes according to the transformer area topology structure, and calculate reactive power transmission loss between nodes;

[0153] Determine a state description of each node according to the characteristic description, the electrical connection relationship and the reactive power transmission loss.

[0154] In one embodiment, the calculation module 202 is configured to:

[0155] According to the mapping relationship between the characteristics and the reactive power compensation demand, the basic compensation demand of each characteristic in each node is calculated, and a plurality of basic compensation demands are weighted and summed to obtain the preliminary reactive power compensation demand of each node;

[0156] The power flow calculation method is used to simulate the state of the power grid after reactive power compensation, and the reactive power distribution results of each node and the voltage out-of-limit information of adjacent nodes are calculated;

[0157] According to the voltage out-of-limit information of adjacent nodes, the preliminary reactive power compensation demand of each node is adjusted.

[0158] In one embodiment, the calculation module 202 is configured to:

[0159] Taking the substation topology structure and the initial reactive power compensation demand of each node as input, the voltage amplitude of each node is iteratively solved by the Newton-Raphson method;

[0160] It is judged whether the voltage amplitude of the target node exceeds the safety threshold, and if the voltage amplitude of the target node exceeds the safety threshold, the target node is determined as an out-of-limit node;

[0161] According to the substation topology structure, the adjacent nodes directly electrically connected with the out-of-limit node are identified;

[0162] Based on historical data and real-time load fluctuation, the probability of voltage out-of-limit of adjacent nodes is predicted.

[0163] In one embodiment, the strategy module 203 is configured to:

[0164] The reactive power compensation device capacity constraint, the voltage safety constraint and the power balance constraint are integrated into the multi-objective optimization function, and the Pareto optimal solution set is generated by the non-dominated sorting genetic algorithm;

[0165] The fuzzy membership function is used to quantify the satisfaction degree of each solution in the Pareto optimal solution set on each target, and the comprehensive satisfaction degree of each solution is calculated by weighting;

[0166] The solution with the highest comprehensive satisfaction degree is selected as the global optimal solution, and the global optimal solution is mapped to the reactive power compensation amount of each node.

[0167] In one embodiment, the execution module 204 is configured to:

[0168] According to the dynamic reactive power demand in the global optimal reactive power compensation strategy and the real-time photovoltaic fluctuation, the model predictive control is used to generate the real-time reactive power output instruction of the static reactive power generator;

[0169] According to the basic reactive power demand in the global optimal reactive power compensation strategy and the load peak-valley period, the fuzzy control is used to generate the grading switching instruction of the capacitor bank;

[0170] According to the remaining reactive power capacity demand and the voltage out-of-limit node in the globally optimal reactive power compensation strategy, a droop control is used to generate the reactive power set value of the inverter;

[0171] According to the three-phase imbalance compensation demand in the globally optimal reactive power compensation strategy, a greedy algorithm is used to generate the action signal of the commutating switch.

[0172] The real-time reactive power output instruction, the grading switching instruction, the reactive power set value and the action signal are sent to each target device.

[0173] It should be noted that: the device provided in the above embodiment, when realizing its function, is only exemplified by the above division of each functional module, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0174] The embodiment also discloses an electronic device, which refers to Figure 3 The electronic device can include at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305.

[0175] The communication bus 302 is used to realize the connection and communication between the components.

[0176] The user interface 303 can include a display screen (Display) and a camera (Camera), and the optional user interface 303 can also include a standard wired interface and a wireless interface.

[0177] The network interface 304 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0178] The processor 301 can include one or more processing cores. The processor 301 connects various parts within the server through various interfaces and lines, executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Alternatively, the processor 301 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes operating systems, user interfaces, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be realized by a separate chip.

[0179] The memory 305 can include a random access memory (RAM) and a read-only memory (ROM). Alternatively, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 305 can alternatively be at least one storage device located away from the aforementioned processor 301. As shown in the figure, the memory 305 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an application program of a kind of transformer substation reactive power optimization method. Figure 3

[0180] In Figure 3 ​In the electronic device shown, the user interface 303 is mainly used to provide an interface for the user to input, and obtain data input by the user; and the processor 301 can be used to call an application program of a power optimization method of a transformer area stored in the memory 305, and when executed by one or more processors 301, make the electronic device execute the method of one or more of the above embodiments.

[0181] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other order or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0182] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0183] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the units is only a logical function division. There can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different parts can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical or other forms.

[0184] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0185] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0186] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory 305 and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory 305 includes: a U disk, a mobile hard disk, a magnetic or optical disk and various media that can store program codes.

[0187] The above-described are only exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the disclosure. The present application is intended to cover any variations, uses or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the art that are not described in the present disclosure. The scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for optimizing reactive power in a transformer substation, characterized in that, The method, applied to a reactive power optimization platform for transformer substations, includes: The system acquires first data from the photovoltaic inverter, second data from the user's electricity meter, and third data from the distribution area. It determines photovoltaic fluctuation characteristics from the first data, load characteristics from the second data, and voltage over-limit characteristics from the third data. The photovoltaic fluctuation characteristics include photovoltaic output fluctuation rate, change slope, and intraday fluctuation amplitude. The load characteristics include three-phase imbalance and peak-valley difference. The voltage over-limit characteristics include the duration, amplitude, and frequency of voltage over-limit. The reactive power compensation requirement for each node is calculated based on the photovoltaic fluctuation characteristics, the load characteristics, the voltage over-limit characteristics, and the transformer area topology. A multi-objective optimization function is constructed based on voltage deviation, curtailment rate, and line loss. A globally optimal reactive power compensation strategy is generated based on the multi-objective optimization function and the reactive power compensation requirements of each node. The global optimal reactive power compensation strategy is decomposed into switching instructions for each reactive power compensation device and executed.

2. The reactive power optimization method for transformer substations according to claim 1, characterized in that, The calculation of reactive power compensation requirements for each node based on the photovoltaic fluctuation characteristics, load characteristics, voltage over-limit characteristics, and transformer area topology includes: Based on the photovoltaic fluctuation characteristics, the load characteristics, the voltage over-limit characteristics, and the transformer area topology, a state description for each node is formed; The initial reactive power compensation requirement of each node is calculated based on the state description, and the initial reactive power compensation requirement of each node is adjusted according to the transformer area topology. The adjusted photovoltaic fluctuation compensation demand, three-phase imbalance compensation demand, and voltage over-limit compensation demand are weighted and summed to obtain the reactive power compensation demand for each node.

3. The reactive power optimization method for transformer substations according to claim 2, characterized in that, The process of forming a state description for each node based on the photovoltaic fluctuation characteristics, the load characteristics, the voltage over-limit characteristics, and the transformer substation topology includes: The photovoltaic fluctuation characteristics are mapped to photovoltaic grid-connected nodes, the load characteristics are mapped to user load nodes, and the voltage over-limit characteristics are mapped to voltage over-limit nodes to determine the characteristic description of each node; Based on the transformer substation topology, determine the electrical connection relationship between each node and other nodes, and calculate the reactive power transmission loss between nodes. The state description of each node is determined based on the feature description, the electrical connection relationship, and the reactive power transmission loss.

4. The reactive power optimization method for transformer substations according to claim 3, characterized in that, The step of calculating the initial reactive power compensation requirement for each node based on the state description, and adjusting the initial reactive power compensation requirement for each node according to the transformer area topology, includes: Based on the mapping relationship between features and reactive power compensation requirements, the basic compensation requirement for each feature in each node is calculated, and the weighted sum of multiple basic compensation requirements is obtained to obtain the preliminary reactive power compensation requirement for each node. The power flow calculation method is used to simulate the power grid state after reactive power compensation, and the reactive power distribution results of each node and the voltage over-limit information of adjacent nodes are calculated. The initial reactive power compensation requirement for each node is adjusted based on the voltage over-limit information of the adjacent nodes.

5. The reactive power optimization method for transformer substations according to claim 4, characterized in that, The calculation results of reactive power distribution at each node and voltage over-limit information of adjacent nodes include: Using the transformer substation topology and the initial reactive power compensation requirements of each node as input, the voltage amplitude of each node is solved iteratively using the Newton-Raphson method. Determine whether the voltage amplitude of a target node exceeds the safety threshold. If the voltage amplitude of a target node exceeds the safety threshold, the target node is determined to be an over-limit node. Based on the transformer area topology, identify the adjacent nodes that are directly electrically connected to the over-limit node; The probability of voltage exceeding the limit of the adjacent node is predicted based on historical data and real-time load fluctuations.

6. The reactive power optimization method for transformer substations according to claim 1, characterized in that, The step of generating the globally optimal reactive power compensation strategy based on the multi-objective optimization function and the reactive power compensation requirement of each node includes: The capacity constraints of the reactive power compensation device, voltage safety constraints, and power balance constraints are incorporated into the multi-objective optimization function, and a Pareto optimal solution set is generated by a non-dominated sorting genetic algorithm. The satisfaction of each solution in the Pareto optimal solution set on each objective is quantified by fuzzy membership function, and the overall satisfaction of each solution is calculated by weighting. The solution with the highest overall satisfaction is selected as the global optimal solution, and the global optimal solution is mapped to the reactive power compensation amount of each node.

7. The reactive power optimization method for transformer substations according to claim 1, characterized in that, The step of decomposing the globally optimal reactive power compensation strategy into switching instructions for each reactive power compensation device and executing them further includes: Based on the dynamic reactive power demand and real-time photovoltaic fluctuations in the global optimal reactive power compensation strategy, model predictive control is used to generate real-time reactive power output commands for the static var generator. Based on the basic reactive power demand and load peak and valley periods in the global optimal reactive power compensation strategy, fuzzy control is used to generate segmented switching instructions for capacitor banks. Based on the remaining reactive capacity requirement and voltage over-limit node in the global optimal reactive power compensation strategy, droop control is used to generate the reactive power setpoint of the inverter. Based on the three-phase imbalance compensation requirement in the global optimal reactive power compensation strategy, a greedy algorithm is used to generate the action signal of the commutation switch. The real-time reactive power output command, the tiered switching command, the reactive power setting value, and the action signal are sent to each target device.

8. A reactive power optimization system for transformer substations, characterized in that, It includes a data acquisition module, a calculation module, a strategy module, and an execution module, among which: The data acquisition module is configured to acquire first data from the photovoltaic inverter, second data from the user's electricity meter, and third data from the distribution area. It determines photovoltaic fluctuation characteristics from the first data, load characteristics from the second data, and voltage over-limit characteristics from the third data. The photovoltaic fluctuation characteristics include photovoltaic output fluctuation rate, change slope, and intraday fluctuation amplitude. The load characteristics include three-phase imbalance and peak-valley difference. The voltage over-limit characteristics include the duration, amplitude, and frequency of voltage over-limit. The calculation module is configured to calculate the reactive power compensation requirement of each node based on the photovoltaic fluctuation characteristics, the load characteristics, the voltage over-limit characteristics, and the transformer area topology. The strategy module is configured to construct a multi-objective optimization function based on voltage deviation, curtailment rate and line loss, and generate a globally optimal reactive power compensation strategy based on the multi-objective optimization function and the reactive power compensation requirements of each node. The execution module is configured to decompose the globally optimal reactive power compensation strategy into switching instructions for each reactive power compensation device and execute them.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The processor is used to execute the instructions stored in the memory so that the electronic device performs the reactive power optimization method for transformer substations as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the reactive power optimization method for transformer substations as described in any one of claims 1-7.

Images